Auxiliary method for diagnosis of lower urinary tract symptoms

Abstract

A method for supporting diagnosis of LUTS may include: receiving urinary system data of an examinee; deriving prediction result data for the examinee by using an LUTS prediction model for the received urination system data; and providing the derived prediction result data to a user terminal.

1 . A method for supporting diagnosis of LUTS (lower urinary tract symptom), comprising:
 receiving urinary system data of an examinee;   deriving prediction result data for the examinee by using an LUTS prediction model for the received urination system data; and   providing the derived prediction result data to a user terminal.   
     
     
         2 . The method of  claim 1 , further comprising:
 learning a correlation between diagnosis result data and urinary system data acquired in advance and stored in a database, through a machine learning algorithm; and   generating an LUTS prediction model based on a result of the learning.   
     
     
         3 . The method of  claim 1 , wherein the urination system data comprises one or more of an age, number of urinations, residual urine volume, uroflowmetry index, prostate symptom score, past medical history and voiding efficacy of the examinee,
 wherein the prediction result data comprises one or more of predicted diagnosis, BOO (Bladder Outlet Obstruction) probability, DUA (Detrusor Under-Activity) probability, and information on whether UDS (Urodynamic Study) is needed.   
     
     
         4 . The method of  claim 2 , wherein the LUTS prediction model further comprises a first neural network for predicting a degree of BOO and a second neural network for predicting the degree of DUA. 
     
     
         5 . The method of  claim 4 , wherein the LUTS prediction model is formed so that the second neural network has an output of the first neural network as an input value thereof, or the first neural network has an output of the second neural network as an input value thereof. 
     
     
         6 . The method of  claim 2 , wherein the machine learning algorithm comprises any one of ANN (Artificial Neural Network), stacked auto-encoder, DNN (Deep Neural Network) and LSTM (Long Short Term Memory). 
     
     
         7 . An LUTS diagnosis supporting system comprising:
 a data input unit configured to receive urinary system data of an examinee;   a diagnosis prediction unit configured to derive prediction result data for the examinee by using an LUTS prediction model for the received urinary system data; and   a result providing unit configured to provide the derived prediction result data to a user terminal.   
     
     
         8 . The LUTS diagnosis supporting system of  claim 7 , further comprising a prediction model generation unit configured to learn a correlation between diagnosis result data and urinary system data acquired in advance and stored in a database, through a machine learning algorithm, and generate an LUTS prediction model based on a result of the learning. 
     
     
         9 . The LUTS diagnosis supporting system of  claim 7 , wherein the urinary system data comprises one or more of an age number of urinations, residual urine volume, uroflowmetry index, prostate symptom score, past medical history and voiding efficacy of the examinee,
 wherein the prediction result data comprises one or more of predicted diagnosis, BOO (Bladder Outlet Obstruction) probability, DUA (Detrusor Under-Activity) probability, and information on whether UDS (Urodynamic Study) is needed.   
     
     
         10 . The LUTS diagnosis supporting system of  claim 8 , wherein the LUTS prediction model further comprises a first neural network for predicting degree of BOO and a second neural network for predicting the degree of DUA. 
     
     
         11 . The LUTS diagnosis supporting system of  claim 10 , wherein the LUTS prediction model is formed so that the second neural network has an output of the first neural network as an input value thereof, or the first neural network has an output of the second neural network as an input value thereof. 
     
     
         12 . The LUTS diagnosis supporting system of  claim 8 , wherein the machine learning algorithm comprises any one of ANN (Artificial Neural Network), stacked auto-encoder, DNN (Deep Neural Network) and LSTM (Long Short Term Memory). 
     
     
         13 . A computer readable recording medium in which a program for implementing the method of  claim 1  is recorded.